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	<title>full fine-tuning &#8211; BIOENGINEER.ORG</title>
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		<title>Mutex-Based Federated Learning Brings Full LLM Fine-Tuning to Edge Devices</title>
		<link>https://bioengineer.org/mutex-based-federated-learning-brings-full-llm-fine-tuning-to-edge-devices/</link>
		
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		<pubDate>Tue, 06 Oct 2026 13:58:40 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[adversarial attack detection]]></category>
		<category><![CDATA[Distributed systems]]></category>
		<category><![CDATA[Edge Computing]]></category>
		<category><![CDATA[federated learning]]></category>
		<category><![CDATA[full fine-tuning]]></category>
		<category><![CDATA[Large Language Models]]></category>
		<category><![CDATA[LoRA]]></category>
		<category><![CDATA[mutex mechanism]]></category>
		<category><![CDATA[TCAB dataset]]></category>
		<category><![CDATA[TinyLlama]]></category>
		<category><![CDATA[TinyLlama-1.1B]]></category>
		<category><![CDATA[VRAM constraints]]></category>
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					<description><![CDATA[Researchers at Marmara University have designed a Mutex-based sequential federated learning architecture that enables full fine-tuning of TinyLlama-1.1B on resource-constrained edge devices, achieving 100 percent training stability and 99.02 percent adversarial attack detection accuracy.]]></description>
		
		
		
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